Fake News: How Big Data And AI Can Help

Recent huge political decisions like Brexit and the U.S. presidential elections surprised journalists and professional predictors alike with their unexpected outcomes. In the aftermath, one major contributor has been identified as “fake news”: stories and memes with no basis in truth that may have been created to sway the outcome of these political events.

One worrying bit of reporting from Das Magazin and Vice suggest that political strategists in both the Brexit campaign and the US presidential election used sophisticated personality tests based on social media activity to feed specific fake news stories to highly targeted segments of the population in order to influence their votes.

Whether or not fake news had an impact on these major political decisions seems to no longer be the question; the question going forward is how can we combat it? Journalists are trained to vet their sources carefully, yet even they can be duped by a particularly persuasive bit of fake news. With senior White House advisors toting the viability of “alternative facts” how does the average news consumer differentiate between what is true and what is false?

Facebook flags fake news

Facebook was one of the biggest scapegoats for the spreading of fake news directly after the US elections. Social media sites like Facebook and Twitter were inarguably some of the main channels for the distribution of this information, and Facebook’s advertising platform makes it easy for fake news creators to spread their misinformation initially (many with the intent of earning revenue from displaying ads on the fake news article).

As a result, Facebook became the first company to try to implement a solution: a new option to flag a news feed item as a false news story. When enough users tag a story as fake, it will appear less in people’s news feeds and carry a warning reading, “Many people on Facebook have reported that this story contains false information.”